{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kats 204 Forecasting with Meta-Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial will introduce the meta-learning framework for forecasting in Kats.  The table of contents for Kats 204 is as follows:\n",
    "\n",
    "1. Overview of Meta-Learning Framework For Forecasting         \n",
    "2. Introduction to `GetMetaData`\n",
    "3. Determining Predictability with `MetaLearnPredictability`\n",
    "4. Model Selection with `MetaLearnModelSelect`       \n",
    "5. Hyperparameter Tuning with `MetaLearnHPT`              \n",
    "    5.1. Initializing `MetaLearnHPT`             \n",
    "    5.2. `MetaLearnHPT` with Default Neural Network Model Structure      \n",
    "    5.3. `MetaLearnHPT` with Custom Neural Network Model Structure\n",
    "\n",
    "For the details of methodology of meta-learning framework and our experiment results, please see the reference https://arxiv.org/pdf/2102.05740.pdf ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We provide two types of tutorial notebooks\n",
    "- **Kats 101**, basic data structure and functionalities in Kats (this tutorial)  \n",
    "- **Kats 20x**, advanced topics, including advanced forecasting techniques, advanced detection algorithms, `TsFeatures`, meta-learning, etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Overview of Meta-Learning Framework For Forecasting\n",
    "\n",
    "Suppose we have a time series and we are looking to build the best possible forecast (with respect to a predefined error metric such as mean absolute error) from the following list of candidate models (and possibly other forecasting models in Kats too):\n",
    "* ARIMA\n",
    "* SARIMA\n",
    "* Holt-Winters\n",
    "* Prophet\n",
    "* Theta\n",
    "* STLF\n",
    "\n",
    "\n",
    "\n",
    "For a single time series, it is straightforward to to do hyperparameter tuning for each of the candidate models with this time series, calculate the error metric, and choose the model that minimizes the error metric.  We have discussed this methodology in detail in Kats 201.  Our basic metadata object, `GetMetaData`, which we will introduce below, also does this calculation to find the best forecasting model for a single time series.\n",
    "\n",
    "However, when we are working with a large number of time series, repeating this process quickly becomes intractable, and for that, we include a meta-learning framework for forecasting.  There are two key model classes, plus one optional one, in our meta-learning framework:\n",
    "\n",
    "1. `MetaLearnModelSelect`: Given the metadata for a time series, predict the best model family (from the candidate models of interest) to forecast the series.  This model is a random forest by default.\n",
    "2. `MetaLearnHPT`: Given a time series and a model type, predict the best parameters for this model.  This model is a neutral network.  \n",
    "3. `MetaLearnPredictability` (optional): Given the metadata for a time series, predict if it is \"predictable\", i.e. if it is possible to forecast with a threshold error.  This model is a random forest by default.\n",
    "\n",
    "For each of these models, you can use labeled training data to build a model or you load a pre-trained model from a file path.  \n",
    "\n",
    "We use the `GetMetaData` object to represent the metadata for a time series in `MetaLearnModelSelect` and `MetaLearnPredictability`.  This tutorial begins with an introduction to the `GetMetaData` object.  Since this object is heavily dependent on `TsFeatures`, if you are not familiar `TsFeatures`, you should check out Kats 203 prior to continuing with this tutorial.  \n",
    "\n",
    "Next we will use labeled time series data from the `m3_meta_data.csv` file to show how to use the `MetaLearnPredictability`, `MetaLearnModelSelect` and `MetaLearnPredictability`.  \n",
    "\n",
    "\n",
    "The sample data in `m3_meta_data.csv` is very small, with 78 labeled examples, so the examples we provide here will not be highly accurate, but they will show you the proper workflow for using the meta-learning framework for forecasting in Kats.\n",
    "\n",
    "\n",
    "The full table of contents for Kats 204 is as follow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Introduction to `GetMetaData`\n",
    "\n",
    "The `GetMetaData` class generates the metadata for any time series.  There are three key components to the the metadata for a time series:\n",
    "1. `features`: the `TsFeatures` dictionary for the time series\n",
    "2. `hpt_res`: a dictionary giving the best hyperparameters for each candidate model and the corresponding error metric for the time series \n",
    "3. `best_model`: the name of the model with the smallest error metric\n",
    "\n",
    "The default error metric is mean absolute error (mae) but this can be controlled with the `error_method` argument in `GetMetaData`.  \n",
    "\n",
    "The list of candidate models that we consider is controlled by the `all_models` argument in `GetMetaData`, which is a dictionary with string names of the candidate models as keys and corresponding model classes as value.\n",
    "\n",
    "with keys equal to the string names of the models as keys  and values equal to the corresponding model class.  The keys in `hpt_res` and the value of `best_model` come from the keys of the `all_models` dictionary.  The default value of `all_models` will include the following six models.\n",
    "\n",
    "1. ARIMA\n",
    "2. SARIMA\n",
    "3. Holt-Winters\n",
    "4. Prophet`\n",
    "5. Theta\n",
    "6. STLF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first example uses the `air_passengers` data set.  We show how to get the metadata for this time series.  We start by loading the time series into a `TimeSeriesData` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "warnings.simplefilter(action='ignore')\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "from kats.consts import TimeSeriesData\n",
    "air_passengers_df = pd.read_csv(\"../kats/data/air_passengers.csv\")\n",
    "\n",
    "air_passengers_df.columns = [\"time\", \"value\"]\n",
    "air_passengers_ts = TimeSeriesData(air_passengers_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can construct the `GetMetaData` object for the `air_passengers` data set time series like follows.  We use all of the default settings except that we use mean absolute percentage error (mape) as our error metric rather than the default of mean absolute error (mae) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.models.metalearner.get_metadata import GetMetaData\n",
    "\n",
    "# create an object MD of class GetMetaData with error method mean absolute percentage error (mape)\n",
    "MD = GetMetaData(data=air_passengers_ts, error_method='mape')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the `all_models` dictionary that is used by default here.  You are allowed to specify your own `all_models` dictionary as long as all the values are classes that extends the abstract class `kats.models.Model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arima': kats.models.arima.ARIMAModel,\n",
       " 'holtwinters': kats.models.holtwinters.HoltWintersModel,\n",
       " 'prophet': kats.models.prophet.ProphetModel,\n",
       " 'theta': kats.models.theta.ThetaModel,\n",
       " 'stlf': kats.models.stlf.STLFModel,\n",
       " 'sarima': kats.models.sarima.SARIMAModel}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MD.all_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `all_params` dictionary will have the same keys as the `all_models` dictionary, and the values are the corresponding parameter class (i.e. a class that extends the class `kats.const.Params`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arima': kats.models.arima.ARIMAParams,\n",
       " 'holtwinters': kats.models.holtwinters.HoltWintersParams,\n",
       " 'prophet': kats.models.prophet.ProphetParams,\n",
       " 'theta': kats.models.theta.ThetaParams,\n",
       " 'stlf': kats.models.stlf.STLFParams,\n",
       " 'sarima': kats.models.sarima.SARIMAParams}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MD.all_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the `get_meta_data` function to calculate all the metadata and output the result as a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "WARNING:fbprophet.models:Optimization terminated abnormally. Falling back to Newton.\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "WARNING:fbprophet.models:Optimization terminated abnormally. Falling back to Newton.\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "WARNING:fbprophet.models:Optimization terminated abnormally. Falling back to Newton.\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "WARNING:fbprophet.models:Optimization terminated abnormally. Falling back to Newton.\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "WARNING:fbprophet.models:Optimization terminated abnormally. Falling back to Newton.\n",
      "WARNING:fbprophet.models:Optimization terminated abnormally. Falling back to Newton.\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
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      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# get meta data as a dictionary\n",
    "air_passengers_metadata = MD.get_meta_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the keys of the metadata dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['hpt_res', 'features', 'best_model', 'search_method', 'error_method'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_metadata.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We explained what `features`, `hpt_res` and `best_model` are above.  This dictionary also includes the `search_method` and `error_method`, which will just be the default values in this case.  We can see these as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search_method: RandomSearch\n",
      "error_method: mape\n"
     ]
    }
   ],
   "source": [
    "print(f\"search_method: {air_passengers_metadata['search_method']}\")\n",
    "print(f\"error_method: {air_passengers_metadata['error_method']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The keys of the `hpt_res` dictionary are name of the candidate model families; they should be the same as the keys for the `all_models` and `all_parameters` dictionaries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['arima', 'holtwinters', 'prophet', 'theta', 'stlf', 'sarima'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_metadata['hpt_res'].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values of the `hpt_res` dictionary are two-element tuples.  The first element is gives the hyperparameters that minimize the error metric.  The second element gives the corresponding minimum error metric.  Let's take a look at these values for ARIMA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'p': 4, 'd': 1, 'q': 1}, 0.10648723141588944)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_metadata['hpt_res']['arima']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can sort the different methods by their error metric as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['stlf', 'prophet', 'holtwinters', 'sarima', 'arima', 'theta']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "methods = list(air_passengers_metadata['hpt_res'].keys())\n",
    "sorted(methods, key = lambda m: air_passengers_metadata['hpt_res'][m][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This suggests that Prophet has the lowest error metric.  Let's confirm that this is what `best_model` indicates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'stlf'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_metadata['best_model']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We constructed the `GetMetaData` object for the `air_passengers` data set with all of the default settings.  Let's take a look at the full set of attributes that can be used to initialize `GetMetadata`.\n",
    "\n",
    "This is the only required attributed: \n",
    "* **data**: TimeSeriesData, the time series for which we calculate the metadata\n",
    "\n",
    "The following attributes are all optional:\n",
    "* **all_models**: `Dict[str, m.Model]`, a dictionary for the candidate model classes.  The key is a string naming the model and each value is a corresponding model class (i.e. a class that extends the abstract class `kats.models.Model`).\n",
    "* **all_params**: `Dict[str, Params]`, a dictionary for the candidate model parameter classes.  The keys are the same as the keys for `all_models` and each value is a corresponding parameter class (i.e. a class that extends the class `kats.const.Params`).\n",
    "* **min_length**: int, the minimal length of time series. We raise a value error if the length of `data` is smaller than `min_length`.  The default value of `min_length` is 30.\n",
    "* **scale**: bool, Whether to rescale the time series by its maximum value; default is true.\n",
    "* **method**: SearchMethodEnum, Search method for hyper-parameters tuning; default is random search in the default parameter space\n",
    "* **executor**: Callable, A parallel executor for parallel processing.  By default, we use Python's native multiprocessing implementation.\n",
    "* **error_method**: str, Type of error metric.  Options are `'mape`', `'smape`',`'mae`', `'mase`', `'mse`', `'rmse`';  default is `'mae'`.\n",
    "* **num_trials**: int, Number of trials for hyperparameter search; default is 5\n",
    "* **num_arm**: optional Number of arms in hyperparameter search; default is 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the remaining examples, we use the sample data in `m3_meta_data.csv` to show how to build meta-learning models.  This sample data set contains the metadata for 78 time series, meaning it has that we need to construct 78 metadata dictionaries like the one we constructed for the `air_passengers` data set.  While 78 metadata objects is certainly too few to develop an accurate meta-learning model and you should use more examples for your own meta-learning models to get high accuracy, these examples will help familiarize you with our meta-learning framework.\n",
    "\n",
    "Loading this data is straightforward is straightforward.  After loading it into a `DataFrame`, we have to do some pre-processing with the `eval` function to ensure that the dictionaries are represented as dictionaries and not as strings.  We demonstrate this as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the metadata into a DataFrame\n",
    "metadata_df = pd.read_csv(\"../kats/data/m3_meta_data.csv\")\n",
    "\n",
    "# We need to do a little pre-processing to make sure the dictionaries are represented as dictionaries\n",
    "# rather than as strings.  This function will do that pre-processing.\n",
    "def change_format(tmp):\n",
    "    tmp['hpt_res']=eval(tmp['hpt_res'])\n",
    "    tmp['hpt_res']['sarima'][0]['seasonal_order'] = eval(tmp['hpt_res']['sarima'][0]['seasonal_order'])\n",
    "    tmp['features']=eval(tmp['features'])\n",
    "    return tmp\n",
    "\n",
    "metadata_df = metadata_df.apply(change_format, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's preview the metadata `DataFrame` we just loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hpt_res</th>\n",
       "      <th>features</th>\n",
       "      <th>best_model</th>\n",
       "      <th>search_method</th>\n",
       "      <th>error_method</th>\n",
       "      <th>idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'arima': ({'p': 5, 'd': 2, 'q': 5}, 0.8932924...</td>\n",
       "      <td>{'length': 68, 'mean': 0.35392156862745106, 'v...</td>\n",
       "      <td>stlf</td>\n",
       "      <td>RandomSearch</td>\n",
       "      <td>mape</td>\n",
       "      <td>N1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'arima': ({'p': 5, 'd': 2, 'q': 1}, 0.3989958...</td>\n",
       "      <td>{'length': 68, 'mean': 0.19995256166982928, 'v...</td>\n",
       "      <td>arima</td>\n",
       "      <td>RandomSearch</td>\n",
       "      <td>mape</td>\n",
       "      <td>N1403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'arima': ({'p': 1, 'd': 2, 'q': 3}, 0.2519500...</td>\n",
       "      <td>{'length': 68, 'mean': 0.48962530217566486, 'v...</td>\n",
       "      <td>holtwinters</td>\n",
       "      <td>RandomSearch</td>\n",
       "      <td>mape</td>\n",
       "      <td>N1404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'arima': ({'p': 2, 'd': 1, 'q': 3}, 0.3146896...</td>\n",
       "      <td>{'length': 68, 'mean': 0.2980870396939263, 'va...</td>\n",
       "      <td>stlf</td>\n",
       "      <td>RandomSearch</td>\n",
       "      <td>mape</td>\n",
       "      <td>N1405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'arima': ({'p': 3, 'd': 1, 'q': 1}, 0.1448053...</td>\n",
       "      <td>{'length': 68, 'mean': 0.5336050082683677, 'va...</td>\n",
       "      <td>arima</td>\n",
       "      <td>RandomSearch</td>\n",
       "      <td>mape</td>\n",
       "      <td>N1406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             hpt_res  \\\n",
       "0  {'arima': ({'p': 5, 'd': 2, 'q': 5}, 0.8932924...   \n",
       "1  {'arima': ({'p': 5, 'd': 2, 'q': 1}, 0.3989958...   \n",
       "2  {'arima': ({'p': 1, 'd': 2, 'q': 3}, 0.2519500...   \n",
       "3  {'arima': ({'p': 2, 'd': 1, 'q': 3}, 0.3146896...   \n",
       "4  {'arima': ({'p': 3, 'd': 1, 'q': 1}, 0.1448053...   \n",
       "\n",
       "                                            features   best_model  \\\n",
       "0  {'length': 68, 'mean': 0.35392156862745106, 'v...         stlf   \n",
       "1  {'length': 68, 'mean': 0.19995256166982928, 'v...        arima   \n",
       "2  {'length': 68, 'mean': 0.48962530217566486, 'v...  holtwinters   \n",
       "3  {'length': 68, 'mean': 0.2980870396939263, 'va...         stlf   \n",
       "4  {'length': 68, 'mean': 0.5336050082683677, 'va...        arima   \n",
       "\n",
       "  search_method error_method    idx  \n",
       "0  RandomSearch         mape  N1402  \n",
       "1  RandomSearch         mape  N1403  \n",
       "2  RandomSearch         mape  N1404  \n",
       "3  RandomSearch         mape  N1405  \n",
       "4  RandomSearch         mape  N1406  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert this metadata `DataFrame` into a list of metadata dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_list = metadata_df.to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Determining Predictability with `MetaLearnPredictability`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before using meta-learning models for model selection and hyper-parameter forecasting, we would like to know if our target time series is predictable.  The `MetaLearnPredictability` module allows us to treat this like a binary classification problem and build a model for it.  We train this model using a list of a metadata and a threshold for the error metric.  We use the threshold to label each metadata dictionary as predictable if and only if the error of it's `best_model` is smaller than the input threshold.  The arguments for `MetaLearnPredictability` are as follows:\n",
    "\n",
    "\n",
    "* **metadata**: A list of dictionaries representing the meta-data of time series (e.g., the meta-data generated by GetMetaData object).  Required unless `load_model=True`.\n",
    "\n",
    "* **threshold**: Float; the threshold for the forecasting error. A time series whose forecasting error of the best forecasting model is higher than the threshold is considered as unpredictable. Default is 0.2.\n",
    "\n",
    "* **load_model**: Boolean; whether or not to load a trained model. Default is False.\n",
    "\n",
    "\n",
    "If we want to train a new predictability model from a list of metadata dictionaries, we should include that list in the `metadata` argument.  If we want to load a trained model, we set `load_data=True` and do ignore the `metadata` argument.  We will provide examples of both below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our example, we are going to use the sample metadata from the `m3_meta_data.csv` file to train a predictability model with `MetaLearnPredictability`.  Then we will use this to predict whether or not `air_passenger` time series can be forecasted (with MAPE at most 0.2).\n",
    "\n",
    "We initialize model using the `metadata_list` we previously generated from `m3_meta_data.csv` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.models.metalearner.metalearner_predictability import MetaLearnPredictability\n",
    "\n",
    "# take the time series with MAPE>=0.2 as unpreditable time series and initial the object\n",
    "mlp=MetaLearnPredictability(metadata_list, threshold=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we train the model, we see a dictionary with performance metrics calculated on the test data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.7142857142857143,\n",
       " 'precision': 1.0,\n",
       " 'recall': 0.6666666666666666,\n",
       " 'f1': 0.8}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use this model to predict if the `air_passenger` time series is predictable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.pred(air_passengers_ts) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This suggests that this model can be forecast with MAPE at most 0.2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the model we trained to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp.save_model(\"mlp.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's re-load our saved model into a new `MetaLearnPredictability` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initiate a new object and load the trained model\n",
    "mlp2 = MetaLearnPredictability(load_model=True)\n",
    "mlp2.load_model(\"mlp.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's use our newly loaded model to repeat the prediction we did on the `air_passenger` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp2.pred(air_passengers_ts) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. **Model Selection with `MetaLearnModelSelect`**\n",
    "\n",
    "The `MetaLearnModelSelect` object allows you to build a predictive model to determine the best forecasting model for a time series.  It is trained using a list of metadata dictionaries.  The arguments for `MetaLearnModelSelect` are as follows:\n",
    "\n",
    "* **metadata**: A list of dictionaries representing the meta-data of time series (e.g., the meta-data generated by GetMetaData object).  Required unless `load_model=True`.\n",
    "\n",
    "* **load_model**: Boolean; whether or not to load a trained model. Default is False.\n",
    "\n",
    "If we want to train a new predictability model from a list of metadata dictionaries, we should include that list in the `metadata` argument.  If we want to load a trained model, we set `load_data=True` and do ignore the `metadata` argument.  We will provide examples of both below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our example, we are going to use the sample metadata from the `m3_meta_data.csv` file to train a selection model with `MetaLearnModelSelect`.  Then we will use this to predict the best forecasting model for the `air_passenger` time series.\n",
    "\n",
    "We initialize model using the `metadata_list` we previously generated from `m3_meta_data.csv` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.models.metalearner.metalearner_modelselect import MetaLearnModelSelect\n",
    "\n",
    "#Initialize the MetaLearnModelSelect object\n",
    "mlms = MetaLearnModelSelect(metadata_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each metadata dictionary includes a `best_model`, and we can take a look at the frquencies of these models using the `count_category` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'stlf': 22,\n",
       "         'arima': 11,\n",
       "         'holtwinters': 16,\n",
       "         'prophet': 7,\n",
       "         'sarima': 17,\n",
       "         'theta': 5})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlms.count_category()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we visualize the data and train the model, it is helpful do some preprocessing.  We can do this with the `preprocess` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre-process the metadata\n",
    "# don't down-sample it to balance the classes\n",
    "# standardize the TsFeatures to have zero mean and unit variance\n",
    "mlms.preprocess(downsample=False, scale=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how the different `TsFeatures` in our metadata objects are correlated with each other by plotting a heatmap, which can be generated using the `plot_corr_heatmap` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlms.plot_corr_heatmap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, it is time to train our model.  By default, we will be fitting a random forest model, but other model types (including GBDT, SVT, KNN, Naive Bayes) can be supported using the `method` parameter in the `train` function.  When we run the `train` function, it outputs a dictionary with the training error and test error for each of the candidate models.  All of these error metrics are MAPE because that is the error metric our metadata is using for this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_error': {'meta-learn': 0.2707505373689409,\n",
       "  'stlf': 0.3164668690753769,\n",
       "  'arima': 0.3808565484061371,\n",
       "  'holtwinters': 0.35895838050655815,\n",
       "  'prophet': 0.34057975782810634,\n",
       "  'sarima': 0.3147370600763581,\n",
       "  'theta': 0.3768810106327116},\n",
       " 'pred_error': {'meta-learn': 0.2012419693547273,\n",
       "  'stlf': 0.22838352240857274,\n",
       "  'arima': 0.23199253819013546,\n",
       "  'holtwinters': 0.2180661580785269,\n",
       "  'prophet': 0.21861763287506944,\n",
       "  'sarima': 0.21926918713034252,\n",
       "  'theta': 0.21741147130669883},\n",
       " 'clf_accuracy': 0.625}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train a modelselect model using random forest algorithm   \n",
    "results=mlms.train()\n",
    "\n",
    "# preview the dictionary\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view this dictionary as a `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meta-learn</th>\n",
       "      <th>stlf</th>\n",
       "      <th>arima</th>\n",
       "      <th>holtwinters</th>\n",
       "      <th>prophet</th>\n",
       "      <th>sarima</th>\n",
       "      <th>theta</th>\n",
       "      <th>error_type</th>\n",
       "      <th>error_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.270751</td>\n",
       "      <td>0.316467</td>\n",
       "      <td>0.380857</td>\n",
       "      <td>0.358958</td>\n",
       "      <td>0.340580</td>\n",
       "      <td>0.314737</td>\n",
       "      <td>0.376881</td>\n",
       "      <td>fit_error</td>\n",
       "      <td>MAPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.201242</td>\n",
       "      <td>0.228384</td>\n",
       "      <td>0.231993</td>\n",
       "      <td>0.218066</td>\n",
       "      <td>0.218618</td>\n",
       "      <td>0.219269</td>\n",
       "      <td>0.217411</td>\n",
       "      <td>pred_error</td>\n",
       "      <td>MAPE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   meta-learn      stlf     arima  holtwinters   prophet    sarima     theta  \\\n",
       "0    0.270751  0.316467  0.380857     0.358958  0.340580  0.314737  0.376881   \n",
       "1    0.201242  0.228384  0.231993     0.218066  0.218618  0.219269  0.217411   \n",
       "\n",
       "   error_type error_metric  \n",
       "0   fit_error         MAPE  \n",
       "1  pred_error         MAPE  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df=pd.DataFrame([results['fit_error'], results['pred_error']])\n",
    "results_df['error_type']=['fit_error', 'pred_error']\n",
    "results_df['error_metric']='MAPE'\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's use our trained model to predict the best model for the `air_passengers` time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'stlf'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlms.pred(air_passengers_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the model we trained to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlms.save_model(\"mlms.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's re-load our saved model into a new `MetaLearnModelSelect` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlms2 = MetaLearnModelSelect(load_model=True)\n",
    "mlms2.load_model(\"mlms.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's use our newly loaded model to repeat the prediction we did on the `air_passenger` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'stlf'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlms2.pred(air_passengers_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. **Hyperparameter Tuning with `MetaLearnHPT`**\n",
    "\n",
    "The `MetaLearnHPT` object allows you to build a model to predict the best hyperparameters for a time series given a designated forecasting model.  Specifically, `MetaLearnHPT` builds a neural network model that takes the `TsFeatures` for a time series as inputs and predicts the best hyperparameters for the forecasting model.\n",
    "\n",
    "Since a metadata dictionary contains both the `TsFeatures` and the best parameters (with keys `features` and `hpt_res`, respectively), we can use a list of metadata dictionaries to build this predictive model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our example, we use `metadata_list`, which contains the metadata from the `m3_meta_data.csv` file, to build a  model for the Holt-Winters parameters for a time series.  We then use this model to predict the best Holt-Winters parameters for the `air_passengers` time series.  While this example is using the Holt-Winters model as the designated model, the same process can be used for any forecasting model supported by Kats as long as it is included in our metadata objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Initializing `MetaLearnHPT`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To initialize the `MetaLearnHPT` model, we need to input the `TsFeatures` and hyperparameters for the Holt-Winters model as `DataFrame` objects.  To extract these from the metadata from `m3_meta_data.csv`, it is easiest use the `DataFrame` we loaded with this data, `metadata_df`.\n",
    "\n",
    "First, let's load the `TsFeatures` from `metadata_df` to a new `DataFrame` and preview it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>entropy</th>\n",
       "      <th>lumpiness</th>\n",
       "      <th>stability</th>\n",
       "      <th>flat_spots</th>\n",
       "      <th>hurst</th>\n",
       "      <th>std1st_der</th>\n",
       "      <th>crossing_points</th>\n",
       "      <th>...</th>\n",
       "      <th>diff2y_pacf5</th>\n",
       "      <th>seas_acf1</th>\n",
       "      <th>seas_pacf1</th>\n",
       "      <th>firstmin_ac</th>\n",
       "      <th>firstzero_ac</th>\n",
       "      <th>holt_alpha</th>\n",
       "      <th>holt_beta</th>\n",
       "      <th>hw_alpha</th>\n",
       "      <th>hw_beta</th>\n",
       "      <th>hw_gamma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68</td>\n",
       "      <td>0.353922</td>\n",
       "      <td>0.045430</td>\n",
       "      <td>0.833179</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.011440</td>\n",
       "      <td>2</td>\n",
       "      <td>0.067969</td>\n",
       "      <td>0.138205</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>5.088309</td>\n",
       "      <td>0.212199</td>\n",
       "      <td>0.071177</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.126924</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.328909e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.186695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68</td>\n",
       "      <td>0.199953</td>\n",
       "      <td>0.027798</td>\n",
       "      <td>0.899969</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0.001403</td>\n",
       "      <td>3</td>\n",
       "      <td>0.031521</td>\n",
       "      <td>0.113224</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>1.407981</td>\n",
       "      <td>-0.085866</td>\n",
       "      <td>-0.060512</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.034220</td>\n",
       "      <td>0.032252</td>\n",
       "      <td>1.052632e-01</td>\n",
       "      <td>1.052632e-01</td>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68</td>\n",
       "      <td>0.489625</td>\n",
       "      <td>0.045509</td>\n",
       "      <td>0.806731</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.014501</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.040535</td>\n",
       "      <td>0.129866</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>1.342792</td>\n",
       "      <td>0.173154</td>\n",
       "      <td>-0.019452</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>0.044327</td>\n",
       "      <td>0.044327</td>\n",
       "      <td>1.052632e-01</td>\n",
       "      <td>1.052632e-01</td>\n",
       "      <td>0.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68</td>\n",
       "      <td>0.298087</td>\n",
       "      <td>0.049313</td>\n",
       "      <td>0.667211</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.026464</td>\n",
       "      <td>1</td>\n",
       "      <td>0.021439</td>\n",
       "      <td>0.094965</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>1.676189</td>\n",
       "      <td>0.330326</td>\n",
       "      <td>-0.217522</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.263158e-02</td>\n",
       "      <td>5.263158e-02</td>\n",
       "      <td>0.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68</td>\n",
       "      <td>0.533605</td>\n",
       "      <td>0.065904</td>\n",
       "      <td>0.823098</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.021781</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.045258</td>\n",
       "      <td>0.152166</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>1.469424</td>\n",
       "      <td>0.207897</td>\n",
       "      <td>0.014251</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.091354e-10</td>\n",
       "      <td>9.692337e-12</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   length      mean       var   entropy  lumpiness  stability  flat_spots  \\\n",
       "0      68  0.353922  0.045430  0.833179   0.000461   0.011440           2   \n",
       "1      68  0.199953  0.027798  0.899969   0.000377   0.001403           3   \n",
       "2      68  0.489625  0.045509  0.806731   0.000044   0.014501           2   \n",
       "3      68  0.298087  0.049313  0.667211   0.000149   0.026464           1   \n",
       "4      68  0.533605  0.065904  0.823098   0.000124   0.021781           2   \n",
       "\n",
       "      hurst  std1st_der  crossing_points  ...  diff2y_pacf5  seas_acf1  \\\n",
       "0  0.067969    0.138205               32  ...      5.088309   0.212199   \n",
       "1  0.031521    0.113224               33  ...      1.407981  -0.085866   \n",
       "2 -0.040535    0.129866               24  ...      1.342792   0.173154   \n",
       "3  0.021439    0.094965               25  ...      1.676189   0.330326   \n",
       "4 -0.045258    0.152166               21  ...      1.469424   0.207897   \n",
       "\n",
       "   seas_pacf1  firstmin_ac  firstzero_ac  holt_alpha  holt_beta      hw_alpha  \\\n",
       "0    0.071177            1            12    0.126924   0.000000  2.328909e-01   \n",
       "1   -0.060512            1             3    0.034220   0.032252  1.052632e-01   \n",
       "2   -0.019452            2            18    0.044327   0.044327  1.052632e-01   \n",
       "3   -0.217522            3            23    0.000007   0.000000  5.263158e-02   \n",
       "4    0.014251            2            22    0.000064   0.000000  7.091354e-10   \n",
       "\n",
       "        hw_beta  hw_gamma  \n",
       "0  0.000000e+00  0.186695  \n",
       "1  1.052632e-01  0.263158  \n",
       "2  1.052632e-01  0.210526  \n",
       "3  5.263158e-02  0.157895  \n",
       "4  9.692337e-12  0.000000  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_features_df = pd.DataFrame(metadata_df['features'].tolist())\n",
    "metadata_features_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's do the same for the the Holt-Winters hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seasonal_periods</th>\n",
       "      <th>trend</th>\n",
       "      <th>damped</th>\n",
       "      <th>seasonal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>additive</td>\n",
       "      <td>True</td>\n",
       "      <td>multiplicative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>multiplicative</td>\n",
       "      <td>True</td>\n",
       "      <td>multiplicative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>additive</td>\n",
       "      <td>True</td>\n",
       "      <td>additive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>multiplicative</td>\n",
       "      <td>False</td>\n",
       "      <td>multiplicative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>multiplicative</td>\n",
       "      <td>False</td>\n",
       "      <td>additive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seasonal_periods           trend  damped        seasonal\n",
       "0                 7        additive    True  multiplicative\n",
       "1                 7  multiplicative    True  multiplicative\n",
       "2                14        additive    True        additive\n",
       "3                 4  multiplicative   False  multiplicative\n",
       "4                10  multiplicative   False        additive"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_hpt_df = pd.DataFrame(metadata_df['hpt_res'].map(lambda x: x['holtwinters'][0]).tolist())\n",
    "metadata_hpt_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The arguments for `MetaLearnHPT` are:\n",
    "* **data_x**: pd.DataFrame; A DataFrame with the TsFeatures.  Required unless `load_model=True`.\n",
    "* **data_y**: pd.DataFrame; A DataFrame with the best hyperparameters.  Required unless `load_model=True`.\n",
    "* **default_model**: string; The name of the forecast model whose default settings will be used. Supported options are 'arima', 'sarima', 'theta', 'prophet', 'holtwinters', 'stlf' and None.  Default is None, in which case we instantiate a custom model and use `categorical_idx` and `numerical_idx` to get the names of the hyperparameters.\n",
    "* **categorical_idx**: A list of strings of the names of the categorical hyper-parameters.  Required only when `default_model` is `None` and there are categorical hyper-parameters.\n",
    "* **numerical_idx**: Optional; A list of strings of the names of the numerical hyper-parameters.  Required only when `default_model` is `None` and there are numerical hyper-parameters.\n",
    "    If None, then a customized model will be initiated.\n",
    "* **load_model**: Boolean; whether or not to load a trained model. Default is False.\n",
    "\n",
    "\n",
    "We can initialize the `MetaLearnHPT` model using a `default_model` as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.models.metalearner.metalearner_hpt import MetaLearnHPT\n",
    "\n",
    "mlhpt_holtwinters = MetaLearnHPT(\n",
    "    data_x=metadata_features_df,\n",
    "    data_y=metadata_hpt_df,\n",
    "    default_model='holtwinters'\n",
    ")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We can also initialize this as a custom model by specifying the names of the parameters rather than the name of the model.  The main difference between these formulations is as follows:\n",
    "\n",
    "* When using a default model, you need to use the default neural network structure \n",
    "* When using a custom model, you need to specify the neural network parameters.\n",
    "\n",
    "We will show an example of each of these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlhpt_holtwinters2=MetaLearnHPT(\n",
    "    data_x=metadata_features_df,\n",
    "    data_y=metadata_hpt_df,\t\n",
    "    categorical_idx = [\"trend\",\"damped\",\"seasonal\"],\n",
    "    numerical_idx = [\"seasonal_periods\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 `MetaLearnHPT` with Default Neural Network Model Structure\n",
    "\n",
    "When using a default model like we did when initializing `mlhpt_holtwinters`, `MetaLearnHPT` builds a neural network with the default neural network model structure.  This means we call the `build_network` function with no parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-task neural network structure:\n",
      "MultitaskNet(\n",
      "  (shared_layer): ModuleList(\n",
      "    (0): Linear(in_features=40, out_features=20, bias=True)\n",
      "  )\n",
      "  (cat_layer_combo): ModuleList(\n",
      "    (0): ModuleList(\n",
      "      (0): Linear(in_features=20, out_features=2, bias=True)\n",
      "      (1): Linear(in_features=2, out_features=2, bias=True)\n",
      "    )\n",
      "    (1): ModuleList(\n",
      "      (0): Linear(in_features=20, out_features=3, bias=True)\n",
      "      (1): Linear(in_features=3, out_features=2, bias=True)\n",
      "    )\n",
      "    (2): ModuleList(\n",
      "      (0): Linear(in_features=20, out_features=5, bias=True)\n",
      "      (1): Linear(in_features=5, out_features=2, bias=True)\n",
      "    )\n",
      "    (3): ModuleList(\n",
      "      (0): Linear(in_features=20, out_features=3, bias=True)\n",
      "      (1): Linear(in_features=3, out_features=6, bias=True)\n",
      "    )\n",
      "  )\n",
      "  (num_layer): ModuleList()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "mlhpt_holtwinters.build_network()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the `train` function to train the neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlhpt_holtwinters.train(lr=0.001, batch_size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the training curves for this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlhpt_holtwinters.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use our trained model to predict the best Holt-Winters parameters for the `air_passengers` time series.  The `pred` function returns a `DataFrame` and the predicted parameters are in the `parameters` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trend': 'multiplicative',\n",
       " 'damped': False,\n",
       " 'seasonal': 'additive',\n",
       " 'seasonal_periods': 24}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=mlhpt_holtwinters.pred(air_passengers_ts)\n",
    "pred['parameters'].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the model we trained to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlhpt_holtwinters.save_model(\"mlhpt_hw.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's re-load our saved model into a new `MetaLearnHPT` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlhpt_holtwinters3=MetaLearnHPT(load_model=True)\n",
    "mlhpt_holtwinters3.load_model(\"mlhpt_hw.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use our newly loaded model to repeat the prediction we did on the `air_passenger` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trend': 'multiplicative',\n",
       " 'damped': False,\n",
       " 'seasonal': 'additive',\n",
       " 'seasonal_periods': 24}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=mlhpt_holtwinters3.pred(air_passengers_ts)\n",
    "pred['parameters'].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 `MetaLearnHPT` with Custom Neural Network Model Structure\n",
    "\n",
    "When using a custom model like we did when initializing `mlhpt_holtwinters2`, you need to specify the model structure by providing the parameters for the neural network to the `build_network` function. \n",
    "\n",
    "Here's how we can do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-task neural network structure:\n",
      "MultitaskNet(\n",
      "  (shared_layer): ModuleList(\n",
      "    (0): Linear(in_features=40, out_features=50, bias=True)\n",
      "  )\n",
      "  (cat_layer_combo): ModuleList(\n",
      "    (0): ModuleList(\n",
      "      (0): Linear(in_features=50, out_features=20, bias=True)\n",
      "      (1): Linear(in_features=20, out_features=10, bias=True)\n",
      "      (2): Linear(in_features=10, out_features=2, bias=True)\n",
      "    )\n",
      "    (1): ModuleList(\n",
      "      (0): Linear(in_features=50, out_features=20, bias=True)\n",
      "      (1): Linear(in_features=20, out_features=10, bias=True)\n",
      "      (2): Linear(in_features=10, out_features=2, bias=True)\n",
      "    )\n",
      "    (2): ModuleList(\n",
      "      (0): Linear(in_features=50, out_features=20, bias=True)\n",
      "      (1): Linear(in_features=20, out_features=2, bias=True)\n",
      "    )\n",
      "  )\n",
      "  (num_layer): ModuleList(\n",
      "    (0): Linear(in_features=50, out_features=30, bias=True)\n",
      "    (1): Linear(in_features=30, out_features=1, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "mlhpt_holtwinters2.build_network(\n",
    "    #One shared one-layer NN with 50 neurons.\n",
    "    n_hidden_shared=[50],\n",
    "    #Each classification task has its own task-specific NN. In this example, \"trend\" and \"dampled\" both have a two-layer NN respectively\n",
    "    #and \"seasonal\" has a one-layer NN.\n",
    "    n_hidden_cat_combo=[[20, 10], [20, 10], [20]], \n",
    "    #One task-specific one-layer NN with 30 neurons for regression task.\n",
    "    n_hidden_num=[30]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use the `train` function to train the model.  We include some of the extra parameters here to specify how to train the neural network model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train the customized NN\n",
    "mlhpt_holtwinters2.train(    \n",
    "    #loss_scale is used to balance 2 types of losses: cross-entropy for classification tasks and MSE for regression tasks\n",
    "    loss_scale=30,\n",
    "    #learning rate\n",
    "    lr=0.005,\n",
    "    n_epochs=2000,\n",
    "    batch_size=16,\n",
    "    #supports ADAM and SGD\n",
    "    method='SGD',\n",
    "    #momentum in SGD.\n",
    "    momentum=0,\n",
    "    #early stop option.\n",
    "    n_epochs_stop=50,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the training curves for this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlhpt_holtwinters2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use our trained model to predict the best parameters for the `air_passengers` time series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trend': 'multiplicative',\n",
       " 'damped': True,\n",
       " 'seasonal': 'multiplicative',\n",
       " 'seasonal_periods': 181.90002}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=mlhpt_holtwinters2.pred(air_passengers_ts)\n",
    "pred['parameters'].iloc[0]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "metadata": {
   "interpreter": {
    "hash": "5b6e8fba36db23bc4d54e0302cd75fdd75c29d9edcbab68d6cfc74e7e4b30305"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}